Nonparametric variational inference

Understand

Variational methods are widely used for approximate posterior inference.

• However, their use is typically limited to families of distributions that enjoy particular conjugacy properties.
• To circumvent this limitation, we propose a family of variational approximations inspired by nonparametric kernel density estimation.
• The locations of these kernels and their bandwidth are treated as variational parameters and optimized to improve an approximate lower bound on the marginal likelihood of the data.